Christian Arzate

Christian Arzate
Ritsumeikan University

PhD

About

13
Publications
4,166
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76
Citations

Publications

Publications (13)
Conference Paper
Fig. 1: Our interactive explanations framework. Abstract-Reinforcement learning techniques successfully generate convincing agent behaviors, but it is still difficult to tailor the behavior to align with a user's specific preferences. What is missing is a communication method for the system to explain the behavior and for the user to repair it. In...
Preprint
Full-text available
In this paper, we propose a generic framework that enables game developers without knowledge of machine learning to create bot behaviors with playstyles that align with their preferences. Our framework is based on interactive reinforcement learning (RL), and we used it to create a behavior authoring tool called MarioMix. This tool enables non-exper...
Conference Paper
Interactive reinforcement learning (RL) has been successfully used in various applications in different fields, which has also motivated HCI researchers to contribute in this area. In this paper, we survey interactive RL to empower human-computer interaction (HCI) researchers with the technical background in RL needed to design new interaction tech...
Article
Full-text available
Player-centered approaches that aim to maximize player enjoyment have been steady, but with poor heuristics that do not rely on any particular theory of entertainment. Certainly, the Theory of Flow is the most referred in the game AI area and, still, it is unclear how to effectively design and implement adaptive game modules or understanding which...
Article
Full-text available
The creation of believable behaviors for Non-Player Characters (NPCs) is key to improve the players' experience while playing a game. To achieve this objective, we need to design NPCs that appear to be controlled by a human player. In this paper, we propose a hierarchical reinforcement learning framework for believable bots (HRLB^2). This novel app...
Chapter
Reinforcement Learning (RL) is a machine learning approach based on how humans and animals learn new behaviors by actively exploring their environment that provides them positive and negative rewards. The interactive RL approach incorporates a human-in-the-loop that can guide a learning RL-based agent to personalize its behavior and/or speed up its...
Preprint
Full-text available
Reinforcement learning techniques successfully generate convincing agent behaviors, but it is still difficult to tailor the behavior to align with a user's specific preferences. What is missing is a communication method for the system to explain the behavior and for the user to repair it. In this paper, we present a novel interaction method that us...
Preprint
Full-text available
Interactive reinforcement learning (RL) has been successfully used in various applications in different fields, which has also motivated HCI researchers to contribute in this area. In this paper, we survey interactive RL to empower human-computer interaction (HCI) researchers with the technical background in RL needed to design new interaction tech...
Conference Paper
In this paper, we propose a generic framework that enables game developers without knowledge of machine learning to create bot behaviors with playstyles that align with their preferences. Our framework is based on interactive reinforcement learning (RL), and we used it to create a behavior authoring tool called MarioMix. This tool enables non-exper...
Conference Paper
Game designers take into account the wide range of play-styles and skill levels of players to create enjoyable experiences. One important step in the game design process involves playtests with professional testers; this process is time-consuming and expensive. Hence, there exist several methods to create synthetic testers to test a game automatica...
Thesis
Full-text available
From an artificial intelligence standpoint, creating adaptive games that maximize players' enjoyment has remained a challenge since it is unclear how to effectively design and implement adaptive game modules or understanding which game features should be adjusted to achieve this objective. In order to address these challenges, in this thesis, we pr...
Article
Full-text available
Resumen—Se usa una cámara de profundidad de bajo costo para detectar, de manera robusta, manos y dedos. Nuestro método propuesto está basado en filtros de profundidad y análisis de contorno adaptivo. La mayor parte del enfoque propuesto se programó en CUDA. Seleccionamos CUDA para poder conseguir una aplicación de visión computacional en tiempo rea...

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Projects

Projects (2)
Project
Human-computer Interaction + Artificial Intelligence
Project
Use of AI in Video Games to create Serious Games